title: Predicting human West Nile virus infections with mosquito surveillance data.
abstract: West Nile virus (WNV) has become established across the Americas with recent heightened activity causing significant human illness. Surveillance methods to predict the risk of human infection are urgently needed to initiate timely preventative measures and justify the expense of implementing costly or unpopular control measures, such as aerial spraying or curfews. We quantified the links between mosquito surveillance data and the spatiotemporal patterns of 3,827 human WNV cases reported over 5 years in Colorado from 2003 to 2007. Mosquito data were strongly predictive of variation in the number of human WNV infections several weeks in advance in both a spatiotemporal statewide analysis and temporal variation within counties with substantial numbers of human cases. We outline several ways to further improve the predictive power of these data and we quantify the loss of information if no funds are available for testing mosquitoes for WNV. These results demonstrate that mosquito surveillance provides a valuable public health tool for assessing the risk of human arboviral infections, allocating limited public health resources, and justifying emergency control actions.
authors.affiliation: Department of Ecology and Evolutionary Biology, 1156 High Street, University of California-Santa Cruz, CA 95064, USA. akilpatr@ucsc.edu
authors.forename: A Marm
authors.initials: AM
authors.lastname: Kilpatrick
authors.forename: W John
authors.initials: WJ
authors.lastname: Pape
mesh_terms: D000818:Animals; D003120:Colorado / Q000453:epidemiology; D003465:Culex / Q000821:virology*; D006801:Humans; D007303:Insect Vectors / Q000821:virology*; D018571:Sentinel Surveillance; D062211:Spatio-Temporal Analysis; D014901:West Nile Fever / Q000453:epidemiology*
keywords: Lyme disease; arbovirus; disease control; eastern equine encephalitis virus; predictive model; public health; vector index